1. Data Processing Algorithms for Analysis of High Resolution MSMS Spectra of Peptides with Complex Patterns of Posttranslational Modifications Shenheng Guan and Alma L. Burlingame

2. Problem Input: An MS/MS spectrum of a mixture of peptides:  Heavily modified protein  Same amino acid sequence  Same PTM  Same total number of PTMs  Different PTM configurations Example  Two peptides with two methylations each. LATK[+32]AARKSAE LATK[+16]AARK[+16]SAE Problem:  Identify the PTM configurations  Estimate their relative abundance

3. Work flow

4. Peptide identification Input  A deisotoped MS/MS spectrum of a mixture of peptides  An identified peptide, the type of PTMs and the number of PTMs. Example  Peptide: LATKAARKSAPATGGVKKPHRYRPGTVALRE  PTM: Methylation  #PTM: 4 Problem  Identify the PTM configurations  Estimate their relative abundance

5. All possible configuration Assumption:  All methylations are on lysine residues  Each lysine residue has at most 3 methyl groups.

6. Configuration identification Score of Spectrum-Configuration-Pair  Spectrum S: ETD peak list  Configuration C: theoretical peak list (c-ion)  Sc(S,C) is the number of matched peaks in the real peak list and the theoretical peak list. Greedy algorithm  Compute the matching score for each configuration  Remove the configure with the highest score from the configuration set and remove the peaks in S that are matched to the configuration  Repeat the above steps until all configurations have score 0

7. Configuration identification results

8. Estimation of relative abundance We have four identified configurations C 1,C 2,C 3,C 4. x 1, x 2, x 3, x 4 the relative abundance  Sum equals to 1 Consider the ith c-ion with charge z  Five possible peaks p 0, …, p 4  Suppose p 2 is matched to C 1, C 2  Observed peak intensity I(p 2 )  Theoretical peak intensity Compute the observed and theoretical peak intensity pair for each matched c-ion

9. Estimation of relative abundance Find x 1, x 2, x 3, x 4 such that the sum of the squared errors of these intensity pairs is minimized. Standard non-negative least-square procedure

10. A Novel Approach for Untargeted Post- translational Modification Identification Using Integer Linear Optimization and Tandem Mass Spectrometry Richard C. Baliban, Peter A. DiMaggio, Mariana D. Plazas-Mayorca, Nicolas L. Young, Benjamin A. Garcia and Christodoulos A. Floudas

11. Bottom up PTM identification Two approaches  Tags  Non-tags Restricted Unrestricted  PILOT_PTM

12. Preprocessing Remove all peaks related the precursor ion Only keep locally significant peaks Deisotope Remove neutral offset if the peak doe not have a complementary peak. Each candidate peak has a list of supporting peaks.

13. ILP Model Input  A preprocessed deisotoped spectrum S={ a 1,a 2,…,a m }  A peptide (theoretical b-ion peak list) P={ b 1 b 2 …b n }  A list of all known PTMs Theoretical peak b k  CS k is the set of all possible peaks (indices) in S that b k can be matched to with PTMs Real peak a j  Pos j is the set of all possible peaks (indices) in P that a j can be matched to with PTMs  Support j is the set of all peaks (indices) supporting peak j in S  Mult j is the set of all peaks (indices) peak j supports

14. ILP Model Binary variable  p j,k = 1 if peak a j in S is matched to b k in P, otherwise p j,k = 0  y j = 1 is peak a j is a supporting peak or matched peak, otherwise y j = 0

15. ILP Model Objective Subject to  One peak in P can only match one peak in S  One peak in S can only match one peak in P

16. ILP Model Subject to: No three consecutive missing peaks The intensity of peak i is counted iff the exists one peak j such that peak i supports j and peak j is a matched peak.

17. ILP Model Solve using CPLEX  Report top-10 variable assignments Existing problem  No constraints that require the distance between two neighboring matched peaks should match the mass of a residue (with PTM)

18. New constraints For each p j,k  Set of candidate ion peaks j’ with respect to k’ such that no valid jump exists between j and j’  The maximum and minimum masses that can be reached from j, respectively

19. New constraints Neighboring matched peaks do not conflict Conflicting matched peaks must have a matched peak between them The distance between two matched peaks should be bounded

20. Postprocessing Re-scoring 10 candidate modified candidate peptides  Cross-correlation score Recheck modifications if there are unmatched peaks indicating non- modification

21. Test data sets Test set A: 44 CID spectra (Ion trap), 174 ETD spectra (Orbitrap) of chemically synthesized phosphopeptides, manually validated Test set B: 58 ECD spectra (FTICR) of Histone H3-(1–50) N-terminal Tail, manually validated Test set C: 553 CID spectra (Orbitrap) of Propionylated Histone Fragments, manually validated Test set D: 525 modified and 6025 unmodified CID spectra (Orbitrap) from chromatin fraction. Identified by SEQUEST and validated by MASCOT and remove low quality spectra manually Test set E: unmodified 36 (Ion trap), 37 (Q-TOF), 4061(Orbitrap) CID unmodified spectra. Validated as test set D

22. Residue predication accuracy

23. Peptide prediction accuracy

24. Comparison on test sets C and D1 Peptide and residue prediction accuracy

25. Comparison on test sets C and D1 Subsequence prediction accuracy

26. Running time

27. Q & A